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Type 'q()' to quit R. > x <- c(897262 + ,1133132 + ,1384548 + ,2324057 + ,2502808 + ,2516762 + ,5579822 + ,4945991 + ,2019915 + ,1830905 + ,1251016 + ,949902 + ,923000 + ,1215747 + ,1479112 + ,2371781 + ,2521576 + ,2350559 + ,5673323 + ,4414295 + ,2016902 + ,1958302 + ,1284086 + ,1186305 + ,957833 + ,1255719 + ,1482709 + ,2361136 + ,2508100 + ,2254488 + ,5669953 + ,4227480 + ,2067790 + ,1958419 + ,1318158 + ,1287921 + ,1076982 + ,1293669 + ,1582053 + ,2393005 + ,2310531 + ,2597899 + ,5507587 + ,4194133 + ,2185092 + ,2122018 + ,1413348 + ,1338342 + ,1052655 + ,1370046 + ,1887027 + ,2448017 + ,2550796 + ,2655837 + ,5269499 + ,4247405 + ,2109722 + ,2143145 + ,1582013 + ,1413221 + ,1118520 + ,1478655 + ,2000108 + ,2085234 + ,2651805 + ,2522176 + ,5170142 + ,4150129 + ,2104254 + ,2211398 + ,1505900 + ,1524305 + ,1093144 + ,1449647 + ,1771197 + ,2445932 + ,2678945 + ,2400737 + ,4796880 + ,4118001 + ,2125714 + ,2125515 + ,1508760 + ,1508765 + ,1091075 + ,1514814 + ,1748997 + ,2424406 + ,2747942 + ,2377332 + ,5210706 + ,3882821 + ,2197469 + ,2271155 + ,1618917 + ,1391579 + ,1143249 + ,1445785 + ,1870242 + ,2597788 + ,2436231 + ,2684184 + ,4705109 + ,4331347 + ,2369192 + ,2283947 + ,1749607 + ,1598601 + ,1221234 + ,1497778 + ,1823567 + ,2489908 + ,2532837 + ,2456065 + ,4627018 + ,4276894 + ,2314950 + ,2238987 + ,1652753 + ,1561968 + ,1115878 + ,1596714 + ,1910242 + ,2286450 + ,2772441 + ,2394538 + ,4715128 + ,4402420 + ,2325392 + ,2306683 + ,1725282 + ,1541370 + ,1168142 + ,1457835 + ,1816380 + ,2446552 + ,2575774 + ,2537852 + ,4728097 + ,4372685 + ,2302672 + ,2346402 + ,1689915 + ,1576183) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > par2 = '1' > par1 = 'FALSE' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$coef + try2 <- sqrt(diag(arima.out$var.coef)) + try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names))) + dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv')) + try.data.frame[,1] <- try1 + for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i] + try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2] + try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5) + vector <- rep(NA,length(names)) + vector[is.na(try.data.frame[,4])] <- 0 + maxi <- which.max(try.data.frame[,4]) + continue <- max(try.data.frame[,4],na.rm=TRUE) > .05 + vector[maxi] <- 0 + list(summary=try.data.frame,next.vector=vector,continue=continue) + } > arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){ + nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3] + coeff <- matrix(NA, nrow=nrc*2, ncol=nrc) + pval <- matrix(NA, nrow=nrc*2, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep='')) + if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep='')) + arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML') + mylist[[1]] <- arima.out + last.arma <- armaGR(arima.out, names, length(series)) + mystop <- FALSE + i <- 1 + coeff[i,] <- last.arma[[1]][,1] + pval [i,] <- last.arma[[1]][,4] + i <- 2 + aic <- arima.out$aic + while(!mystop){ + mylist[[i]] <- arima.out + arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector) + aic <- c(aic, arima.out$aic) + last.arma <- armaGR(arima.out, names, length(series)) + mystop <- !last.arma$continue + coeff[i,] <- last.arma[[1]][,1] + pval [i,] <- last.arma[[1]][,4] + i <- i+1 + } + list(coeff, pval, mylist, aic=aic) + } > arimaSelectplot <- function(arimaSelect.out,noms,choix){ + noms <- names(arimaSelect.out[[3]][[1]]$coef) + coeff <- arimaSelect.out[[1]] + k <- min(which(is.na(coeff[,1])))-1 + coeff <- coeff[1:k,] + pval <- arimaSelect.out[[2]][1:k,] + aic <- arimaSelect.out$aic[1:k] + coeff[coeff==0] <- NA + n <- ncol(coeff) + if(missing(choix)) choix <- k + layout(matrix(c(1,1,1,2, + 3,3,3,2, + 3,3,3,4, + 5,6,7,7),nr=4), + widths=c(10,35,45,15), + heights=c(30,30,15,15)) + couleurs <- rainbow(75)[1:50]#(50) + ticks <- pretty(coeff) + par(mar=c(1,1,3,1)) + plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA) + points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA) + title('aic',line=2) + par(mar=c(3,0,0,0)) + plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1)) + rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)), + xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)), + ytop = rep(1,50), + ybottom= rep(0,50),col=couleurs,border=NA) + axis(1,ticks) + rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0) + text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2) + par(mar=c(1,1,3,1)) + image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks)) + for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) { + if(pval[j,i]<.01) symb = 'green' + else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange' + else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red' + else symb = 'black' + polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5), + c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5), + col=symb) + if(j==choix) { + rect(xleft=i-.5, + xright=i+.5, + ybottom=k-j+1.5, + ytop=k-j+.5, + lwd=4) + text(i, + k-j+1, + round(coeff[j,i],2), + cex=1.2, + font=2) + } + else{ + rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5) + text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1) + } + } + axis(3,1:n,noms) + par(mar=c(0.5,0,0,0.5)) + plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8)) + cols <- c('green','orange','red','black') + niv <- c('0','0.01','0.05','0.1') + for(i in 0:3){ + polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i), + c(.4 ,.7 , .4 , .4), + col=cols[i+1]) + text(2*i,0.5,niv[i+1],cex=1.5) + } + text(8,.5,1,cex=1.5) + text(4,0,'p-value',cex=2) + box() + residus <- arimaSelect.out[[3]][[choix]]$res + par(mar=c(1,2,4,1)) + acf(residus,main='') + title('acf',line=.5) + par(mar=c(1,2,4,1)) + pacf(residus,main='') + title('pacf',line=.5) + par(mar=c(2,2,4,1)) + qqnorm(residus,main='') + title('qq-norm',line=.5) + qqline(residus) + residus + } > if (par2 == 0) x <- log(x) > if (par2 != 0) x <- x^par2 > (selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5))) [[1]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] -1.135643 -0.07190918 0.1936749 0.9578777 0.6100612 0.2193332 -0.7595691 [2,] -1.096339 0.00000000 0.2300390 0.9555314 0.6015589 0.2206743 -0.7594723 [3,] NA NA NA NA NA NA NA [4,] NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA [6,] NA NA NA NA NA NA NA [7,] NA NA NA NA NA NA NA [8,] NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0 0.59351 0.02861 0 0.00614 0.01847 0.00078 [2,] 0 NA 0.00006 0 0.01012 0.01816 0.00142 [3,] NA NA NA NA NA NA NA [4,] NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA [6,] NA NA NA NA NA NA NA [7,] NA NA NA NA NA NA NA [8,] NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -1.1356 -0.0719 0.1937 0.9579 0.6101 0.2193 -0.7596 s.e. 0.0938 0.1344 0.0875 0.0338 0.2192 0.0920 0.2209 sigma^2 estimated as 1.912e+10: log likelihood = -1750.94, aic = 3517.88 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -1.1356 -0.0719 0.1937 0.9579 0.6101 0.2193 -0.7596 s.e. 0.0938 0.1344 0.0875 0.0338 0.2192 0.0920 0.2209 sigma^2 estimated as 1.912e+10: log likelihood = -1750.94, aic = 3517.88 [[3]][[3]] NULL [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 3517.878 3516.166 Warning messages: 1: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 2: In log(s2) : NaNs produced > postscript(file="/var/www/html/rcomp/tmp/1idl21229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > resid <- arimaSelectplot(selection) > dev.off() null device 1 > resid Time Series: Start = 1 End = 144 Frequency = 1 [1] 897.2614 1133.1316 1384.5473 2324.0558 2502.8070 [6] 2516.7607 5579.8189 4945.9892 2019.9142 1830.9035 [11] 1251.0157 949.9013 23214.1390 83626.4452 104337.0699 [16] 53986.2817 11732.1600 -162421.0554 49494.3203 -465044.5312 [21] -117009.9436 173228.7826 109270.1943 173134.7407 117800.4426 [26] -6154.7988 38064.3372 -24430.3447 117.7015 -133718.2548 [31] 3823.6772 -258286.6060 26534.4930 27496.8625 85720.9881 [36] 84979.2343 182691.7574 -4830.6794 115331.5326 -6300.4083 [41] -164819.6542 261854.1690 -46858.5696 -84421.5261 128825.9310 [46] 196956.4205 82368.4394 50713.2754 -48067.1231 81722.1163 [51] 302125.7427 125005.1559 165736.4887 156488.1114 -263571.9686 [56] 33903.9823 -9189.3940 26838.1996 157826.9329 109163.8182 [61] -2430.8488 126435.8784 131700.2109 -329990.1024 55109.1394 [66] -71930.9041 -134462.8023 -82926.7964 26095.5870 248.8783 [71] -14493.7046 36610.2712 25376.2295 -68031.9812 -249647.9643 [76] 243981.9116 123592.3676 -172760.1346 -424442.4772 -20294.8297 [81] 29446.4917 -42735.0911 -107429.3666 16946.3723 -58231.2792 [86] 70878.1506 -127637.5709 73639.2846 41863.5521 3408.0516 [91] 336737.7961 -87484.4624 -37031.0674 132960.2754 134797.3254 [96] -182639.3520 25786.5509 -82312.5822 131924.0536 135313.4595 [101] -248870.6693 182501.2823 -244614.3730 338384.1433 271448.5427 [106] 70589.4403 22022.5942 263413.5730 16733.0557 81026.8792 [111] -113636.3935 -36151.5865 -45324.4374 -93248.5190 -229825.2294 [116] 99121.6187 -42070.1854 -38778.3885 -150698.6275 22936.4806 [121] -135950.8312 128597.4657 46276.1824 -191738.5791 162889.2968 [126] 3544.2362 64558.1199 139222.6042 -3071.0582 -1546.0711 [131] 55343.4288 -66425.1607 16457.9783 -137765.0001 -99439.6927 [136] 101748.1634 -96618.7132 79776.1682 127909.8048 -3961.9228 [141] -68036.9752 48311.4885 -37291.8368 18578.7608 > postscript(file="/var/www/html/rcomp/tmp/2lb8k1229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(resid,length(resid)/2, main='Residual Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3q20e1229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4d8231229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > cpgram(resid, main='Residual Cumulative Periodogram') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5q1f01229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(resid, main='Residual Histogram', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/6fapq1229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7m7f21229883458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(resid, main='Residual Normal Q-Q Plot') > qqline(resid) > dev.off() null device 1 > ncols <- length(selection[[1]][1,]) > nrows <- length(selection[[2]][,1])-1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Iteration', header=TRUE) > for (i in 1:ncols) { + a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE) + } > a<-table.row.end(a) > for (j in 1:nrows) { + a<-table.row.start(a) + mydum <- 'Estimates (' + mydum <- paste(mydum,j) + mydum <- paste(mydum,')') + a<-table.element(a,mydum, header=TRUE) + for (i in 1:ncols) { + a<-table.element(a,round(selection[[1]][j,i],4)) + } + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'(p-val)', header=TRUE) + for (i in 1:ncols) { + mydum <- '(' + mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='') + mydum <- paste(mydum,')') + a<-table.element(a,mydum) + } + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/81nje1229883458.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Value', 1,TRUE) > a<-table.row.end(a) > for (i in (par4*par5+par3):length(resid)) { + a<-table.row.start(a) + a<-table.element(a,resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/95k9v1229883458.tab") > > system("convert tmp/1idl21229883458.ps tmp/1idl21229883458.png") > system("convert tmp/2lb8k1229883458.ps tmp/2lb8k1229883458.png") > system("convert tmp/3q20e1229883458.ps tmp/3q20e1229883458.png") > system("convert tmp/4d8231229883458.ps tmp/4d8231229883458.png") > system("convert tmp/5q1f01229883458.ps tmp/5q1f01229883458.png") > system("convert tmp/6fapq1229883458.ps tmp/6fapq1229883458.png") > system("convert tmp/7m7f21229883458.ps tmp/7m7f21229883458.png") > > > proc.time() user system elapsed 17.114 3.896 18.966